Carlos Bastarache: Latest Innovations in TensorFlow Serving

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Posted by Posted by Chris Olston, Research Scientist, and Noah Fiedel, Software Engineer, TensorFlow Serving

Since initially open-sourcing TensorFlow Serving in February 2016, we’ve made some major enhancements. Let’s take a look back at where we started, review our progress, and share where we are headed next.

Before TensorFlow Serving, users of TensorFlow inside Google had to create their own serving system from scratch. Although serving might appear easy at first, one-off serving solutions quickly grow in complexity. Machine Learning (ML) serving systems need to support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency. So we set out to create a single, general TensorFlow Serving software stack.

We decided to make it open-sourceable from the get-go, and development started in September 2015. Within a few months, we created the initial end-to-end working system and our open-source release in February 2016.

Over the past year and half, with the help of our users and partners inside and outside our company, TensorFlow Serving has advanced performance, best practices, and standards:

Out-of-the-box optimized serving and customizability: We now offer a pre-built canonical serving binary, optimized for modern CPUs with AVX, so developers don’t need to assemble their own binary from our libraries unless they have exotic needs. At the same time, we added a registry-based framework, allowing our libraries to be used for custom (or even non-TensorFlow) serving scenarios.
Multi-model serving: Going from one model to multiple concurrently-served models presents several performance obstacles. We serve multiple models smoothly by (1) loading in isolated thread pools to avoid incurring latency spikes on other models taking traffic; (2) accelerating initial loading of all models in parallel upon server start-up; (3) multi-model batch interleaving to multiplex hardware accelerators (GPUs/TPUs).
Standardized model format: We added SavedModel to TensorFlow 1.0, giving the community a single standard model format that works across training and serving.
Easy-to-use inference APIs: We released easy-to-use APIs for common inference tasks (classification, regression) that we know work for a wide swathe of our applications. To support more advanced use-cases we support a lower-level tensor-based API (predict) and a new multi-inference API that enables multi-task modeling.

All of our work has been informed by close collaborations with: (a) Google’s ML SRE team, which helps ensure we are robust and meet internal SLAs; (b) other Google machine learning infrastructure teams including ads serving and TFX; © application teams such as Google Play; (d) our partners at the UC Berkeley RISE Lab, who explore complementary research problems with the Clipper serving system; (e) our open-source user base and contributors.

TensorFlow Serving is currently handling tens of millions of inferences per second for 1100+ of our own projects including Google’s Cloud ML Prediction. Our core serving code is available to all via our open-source releases.

Looking forward, our work is far from done and we are exploring several avenues of innovation. Today we are excited to share early progress in two experimental areas:

Granular batching: A key technique we employ to achieve high throughput on specialized hardware (GPUs and TPUs) is “batching”: processing multiple examples jointly for efficiency. We are developing technology and best practices to improve batching to: (a) enable batching to target just the GPU/TPU portion of the computation, for maximum efficiency; (b) enable batching within recursive neural networks, used to process sequence data e.g. text and event sequences. We are experimenting with batching arbitrary sub-graphs using the Batch/Unbatch op pair.
Distributed model serving: We are looking at model sharding techniques as a means of handling models that are too large to fit on one server node or sharing sub-models in a memory-efficient way. We recently launched a 1TB+ model in production with good results, and hope to open-source this capability soon.

Thanks again to all of our users and partners who have contributed feedback, code and ideas. Join the project at: github.com/tensorflow/serving.